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Article

Transcriptome Analysis Reveals the Heat Stress Response Genes by Fire Stimulation in Michelia macclurei Dandy

by
Shujing Wei
1,*,†,
Zhao Song
1,†,
Sisheng Luo
1,†,
Yingxia Zhong
1,
Yufei Zhou
1 and
Ruisen Lu
2,3,*
1
Guangdong Academy of Forestry, Guangzhou 510520, China
2
Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
3
Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Nanjing 210014, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(3), 610; https://doi.org/10.3390/f14030610
Submission received: 16 January 2023 / Revised: 11 March 2023 / Accepted: 16 March 2023 / Published: 19 March 2023
(This article belongs to the Special Issue Genetic Regulation of Growth and Development of Woody Plants)
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Abstract

:
Heat stress due to external heat sources such as fire is an ecological problem for plants. When forest plants suffer from fire, high temperatures cause an array of morphological, physiological, and biochemical changes, which affect growth and development. Michelia macclurei Dandy is an evergreen broad-leaved tree species with the characteristics of fast growth, strong adaptability, and good fire-resistance. Some studies have improved the understanding of how fire behavior affects physiology, function and mortality, but the extreme heat response genes and mechanisms need improved understanding. In this study, we conducted a fire experiment (slight and severe) and RNA-Seq in M. macclure. The de novo assembly obtained 104,052 unigenes, and 48.46% were annotated in at least one public database. Specifically, 4458 and 4810 differentially expressed genes (DEGs) were identified in slight and severe fire treatment groups, respectively. In two treatment groups, 612 unigenes were differentially expressed, which were enriched in ‘oxidoreductase activity’ in the molecular function (MF) category of Gene Ontology (GO) enrichment analysis, suggesting the core role of oxidoreductase activity in response to extremely high temperatures in M. macclurei. In KEGG enrichment analysis of DEGs, the ‘plant hormone signal transduction’ is overrepresented, suggesting that this process plays an important role during heat response in M. macclurei. In the pathways of cytokinine and salicylic acid, some vital DEGs were enriched, which were related to cell division, shoot initiation, and disease resistance, and the potential interactions during heat stress were discussed. Moreover, the DEGs linked to heat stress response were identified, including heat shock factors, stress enhanced protein, signal transduction, photosystem, and major transcription factors. The qRT-PCR examination of various tissues, expression dynamics, and treatments revealed that the genes coding for the heat shock protein HSF30, stress enhanced protein, and photosystem I reaction center subunit II exhibited particularities in leaf tissue. Genes coding for heat shock proteins displayed a distinct expression pattern between fire treatment and conventional heat stress, which could signify the distinctive function of HSPs and the mechanism of heat responses. Altogether, these may interact to respond to fire stress through alterations in cellular processes, signaling transduction, and the synthesis and degradation of response proteins in M. macclurei. The results of this study provide a crucial transcriptional profile influenced by heat stress in M. macclurei, and could be of great use to explore the fire prevention mechanisms of fire-resistant tree species.

1. Introduction

Michelia macclurei Dandy belongs to the Magnoliaceae family, an evergreen broad-leaved tree species with fast growth, high yield, strong adaptability, and good fire-resistance [1]. Because of its advantages of ecological fire protection, it is listed as one of the important high-quality tree species for afforestation in southern China. It can not only be used as an excellent wood, but also for landscaping and as fire protection trees [2]. Some studies have comprehensively evaluated its fire resistance through physical and chemical properties, flammability indicators, and fire resistance indicators. The intense fire resistance of M. macclurei can be characterized by a suite of traits such as high moisture content, low ignition point and lower rough grease content that decrease the likelihood of being injured or killed by fire [3]. There is, however, little information available about the transcriptional regulation of extreme heat stress response genes by fire stimulation in M. macclurei.
Forest fires are a major forestry disaster worldwide, and are also one of the most important natural disturbances affecting the global plant ecosystem [4]. Forest fires have become a selective driver of plant life history traits through a severe disturbance of plant growth and development [5]. Therefore, understanding the mechanisms and responses to extremely high-temperature events may be critically important for the adaptation of climate change and forest fires in tree species. Three classes of forest fires have been recognized, including ground fires, surface fires, and crown fires [6]. In most cases, forest fires are mainly surface fires and their spread is mainly due to the heat released by the combustion of combustibles, such as leaves and trunks of trees. Living trees generally combust from the entire crown, which kills the tree, which is a complex process because of the direct heat or indirect effects [7,8]. Direct damage is caused by heating transfer; when the flames burn a tree, the heating transfer results in tissue necrosis, such as cambium and phloem. Trees can also be killed indirectly due to changes in the soil (such as increased acidity), physiological alteration, insect attack, and pathogenic infection, which lead to weakened and ultimately dead trees after suffering a fire attack [4]. During a forest fire, heat injuries sustained in a fire affect the physiology of trees by transferring the heat into the roots, bole, and crown. Elevated temperatures in trees induce a variety of stress responses and minimize the damaging effects of high temperatures through numerous molecular mechanisms [9].
Heat stress is often defined as the rise in temperature beyond a threshold level for a period of time sufficient to cause irreversible damage to plant growth and development [10]. At extremely high temperatures, severe cellular injury or death may occur quickly by direct injuries [11], including protein denaturation and aggregation, and the increased fluidity of membrane lipids. At moderately high temperatures, slower heat injuries may occur, including protein degradation, the inhibition of protein synthesis, the inactivation of enzymes in organelles, and a loss of membrane integrity [12]. During heat conduction, a series of transcription factors (TFs) and functional genes respond to the heat stimulation by up- or down-regulation. In plants, the heat stress transcription factors (HSFs) control the accumulation of heat shock proteins (HSPs), which have been proven to play a central role in the heat stress response (HSR) in acquired thermotolerance [13,14]. In Arabidopsis, 21 HSFs members have been defined and they comprise three conserved evolutionary classes, A, B and C. Class A members are responsible for the immediate induction of heat shock proteins upon heat shock, such as known genes HSF1 and HSF2. Class B HSFs are involved in the long-term adaptation to heat stress, and they activate the expression of proteins in response to increased temperatures, such as Hsp20 and Hsp30. Class C members are involved in the regulation of chaperone proteins, which are responsible for the folding, unfolding, and refolding of other proteins, i.e., HSP60, HSP70, and HSP90. They are a complex group of molecular proteins produced by cell organisms when exposed to stress or extreme temperatures. They act as a protective response, helping a cell survive acute stress and aiding in its recovery after the stress has gone away [13,14,15]. For example, HsfA1a was defined as a master regulator of HSR in tomato [16], the expression of HsfA2 was shown to be induced by high light and H2O2, and they have an important role under various abiotic stress conditions [17,18].
In addition, multiple signaling pathways are implicated in the HSR, including oxidative stress signaling [19], Ca2+-dependent signaling [20,21], and phytohormones [22]. It was reported that a burst of H2O2 occurred after very short periods at high temperatures, which was correlated with the induction of HS-responsive genes and NADPH oxidase activity [23,24]. In Arabidopsis thaliana, the calmodulin-binding protein kinase 3 (CBK3) was identified as an important component of the heat-shock (HS) signal transduction pathway. It regulated the transcription of HSP genes and the synthesis of HSPs, and controlled the binding activity of HSFs to HSEs. AtCBK3 controls the binding activity of HSFs to heat-shock elements HSEs by the phosphorylation of AtHSFA1a [25]. Several phytohormones, such as abscisic acid (ABA), salicylic acid (SA), and ethylene, have also been related to HS signaling in different species [26,27,28]. There have been many breakthroughs in the plant heat stress response fields; however, due to species diversity, many functional genes’ responses to heat stress in different species remain to be discovered and identified.
The transcriptome has rapid access to obtain all transcripts of a specific organ or tissue in a certain state. It has been widely applied to identify the specifically expressed genes, and revealed the molecular responses to adversity response in plants. Many heat response genes and pathways have been identified in various plants by omics, including transcriptome, metabolome, and proteome [29,30]. For example, genes related to ATPase regulator, chaperone binding, protein modification, and nitrogen metabolic processes were significantly up- or down-regulated by heat stress in long-term heat stress treatment in the switchgrass [31]. A comprehensive study of the Korean fir transcriptome revealed 204 TFs and 189 HSPs that were differentially expressed [32]. The RNA-Seq of Davidia involucrata seedlings identified 32 genes encoding putative HSFs that are associated with the response to heat stress [33]. As a dominant species in subtropical evergreen forests in southern China, M. macclurei has the ability to adapt to many environmental conditions. For example, it responded to the drought by inducing the accumulation of soluble sugar [34]. However, the transcriptomic response to fire stimulation and high heat stress has not yet been characterized. In this study, we performed a combustion experiment to stimulate the strong heat response and then collected the surrounding tissue with the heat response for transcriptome sequencing to identify the potential responsive genes and pathways. The results will allow us to better understand the adaptation mechanisms to heat stress, and will provide valuable genetic resources for further studies of heat tolerance in M. macclurei.

2. Materials and Methods

2.1. Plant Materials and Treatments

The one-year-old seedlings (50–60 cm height, Figure 1, left) of M. macclurei Dandy were planted in Nanjing Botanical Garden Mem. Sun Yat-Sen (Nanjing, China). The fire experiment was conducted when the leaves grew vigorously. The second and third leaves from the top were selected for the experiment. When carrying out the fire experiment, the alcohol lamp was used to burn the leaves’ tips. The burning time was set to 10 s and 20 s, respectively, which were marked as slight and severe fire treatment (T1 and T2 groups, respectively) (Figure 1, right, the second and third leaves). The normal-growing leaves were set as the control group (Figure 1, right, the first leaf). In a slight fire, the leaf tip will be blackened and the surrounding leaves will gradually change color. In a severe fire, the tips may catch fire, accompanied by discoloration of the surrounding tissue. The surrounding leaves were collected for further study, and the sampling area was shown in the red frames (Figure 1). Three biological replicates were set in each group, and three trees were collected for one biological replicate. In order to strengthen the study and further determine the response of key genes to the fire experiment, we also collected other samples for qRT-PCR experiment. This included leaf, bark, and root tissues, and other stress treatments such as 1 h sample after wounding and 1 h sample after 40 °C (conventional heat-stress). In order to detect the gene expression dynamics after fire treatment, the 30 s fire treatment was also conducted for the qRT-PCR. The collected samples were immediately frozen in liquid nitrogen and then stored in a −80 °C freezer for RNA extraction.

2.2. Total RNA Isolation, Library Construction and Illumina Sequencing

The total RNA was extracted from samples using a Plant RNA Extration KiT (Takara, Code No.9769). The RNA integrity was detected using Agilent 2100 bioanalyzer. The mRNAs with polyA were enriched by Oligo (dT) magnetic beads using the qualified RNAs. Then, the mRNA was randomly interrupted with divalent cations in the NEB fragmentation buffer, and the library was constructed according to the normal library construction method of NEB. The steps were conducted as follows: the fragmented mRNAs were used as templates, and random oligonucleotides were used as primers for synthesizing the first strand of cDNA in the M-MuLV reverse transcriptase system. Subsequently, the RNA strand was degraded with RNaseH, and the second strand of cDNA was synthesized under the DNA polymerase I system. The purified double-stranded cDNA was performed by end-repaired, adding A-tailed, and connecting to a sequencing adapter. The cDNA of about 250–300 bp was screened with AMPure XP beads, amplified by PCR and purified again with AMPure XP beads for final library construction.
After the library was constructed, the preliminary quantification was detected using a Qubit2.0 Fluorometer. Then, the production was diluted to 1.5 ng/uL and the insert size detected using the Agilent 2100 bioanalyzer. Once the insert size was as expected, qRT-PCR was used to measure the effective concentration of the library (above 2 nM was needed) for ensuring the library’s quality. Finally, the Illumina Sequencing (NovaSeq 6000 platform) was performed with paired-end 150 bp (PE150) strategy using qualified libraries. The libraries’ construction and sequencing were completed by Novogen Ltd. (Tianjin, China).

2.3. Sequence Assembly and Gene Annotation

The raw sequencing data were performed for quality control before further analysis. The raw data were filtered by removing reads with adapters, reads containing undetermined base N, and low-quality reads to ensure the quality and reliability of data analysis. After raw data filtering, sequencing error rate checking, and GC content distribution detection, the clean reads were obtained for subsequent analysis. Clean reads were assembled into transcripts using Trinity (v2.5.1) [35]. Then, the transcripts were clustered according to the shared reads between transcripts by the Corset program [36]. Combined with the expression levels of transcripts between different samples and the H-Cluster algorithm, the differentially expressed transcripts were separated from the original cluster for a new cluster, which was defined as unigene. The accuracy and integrity of assembled transcripts were then assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) software [37]. To obtain comprehensive gene function information, gene function annotation was performed on transcripts in seven databases, including Nr, Nt, Pfam, KOG, Swiss-prot, KO, and GO. iTAK software (version 1.2) was used for TF prediction, and the identification and classification of TFs followed, as in the description by Perez-Rodriguez et al. (2010) [38] and Jin et al. (2014) [39].

2.4. Gene Expression Level, Differential and Enrichment Analysis

The transcripts assembled by Trinity were used as the reference sequence (Ref); the clean reads of each sample were mapped to the Ref and filtered out the low-quality reads, including the reads with an alignment quality value lower than 10 and the unpaired alignment reads, and the reads were aligned in multiple regions of the genome. The alignment process was performed using RSEM software (v1.2.15) [40] with the parameter mismatch 0 and bowtie2. RSEM was used to count the alignment results of bowtie, and the read count was calculated by the number of reads mapped to a gene. Quantitative analysis of gene expression levels was performed on each sample and merged for the expression matrix of all samples. Finally, the read count was converted into FPKM (expected number of fragments per kilobase of transcript sequence per millions base pairs sequenced).
Based on the gene expression level, the significantly differentially expressed genes between different samples can be screened by statistical analysis. Firstly, the read count was corrected for normalization by sequencing depth. Then, the hypothesis test probability (p-value) was calculated by the statistical model, and finally the multiple hypothesis test correction (BH) was performed to obtain the FDR value (false discovery rate, also used as padj). The FoldChange (FC) value was calculated from the average of the read counts, and the significance of the difference was analyzed for each comparison combination. In general, the two-fold difference in expression between two samples (padj < 0.05) was considered as differentially expressed in order to further explore the correlation between DEGs and biological functions or pathways under different conditions. The DEGs were subjected to enrichment analysis and classified according to the functional categories, including GO or KEGG annotation.

2.5. Quantitative Real-Time PCR (qRT-PCR) Validation

To validate the RNA-seq data, 16 genes were randomly selected and a quantitative Real-Time PCR (qRT-PCR) analysis for CK, T1, and T2 samples performed. In addition, qRT-PCR was also performed on five key genes (heat shock protein, heat shock factor protein HSF30, stress enhanced protein 2, signal transduction response regulator, and photosystem I reaction center subunit II) in eight samples, including three tissues (leaf, bark, and root), across three dynamic treatments (fire treatment for 10 s, 20 s, and 30 s, respectively), and two other stressful conditions (1 h after wounding and 1 h after 40 °C heat stress). A 0.5 μg quantity of total RNA was used for the synthesis of first-strand cDNA using the PrimeScriptTM RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China) according to the manufacturer’s protocol. qRT-PCR primers (Table S1) were designed using Primer3 Release 2.3.4 [41] and synthesized by General Biosystems Co., Ltd. (Anhui, China). qRT-PCR was conducted using SYBR® Premix Ex Taq™ II (Tli RNaseH Plus) (TaKaRa, RR820A, Dalian, China) according to the protocol. The 96 well optical reaction plate was performed on the LightCycler 480 (Roche Molecular Biochemicals, Mannheim, Germany). Three biological and technical replicates were set for the quantification of each gene expression. According to the previous report on the genus Michelia, the GAPDH gene (encoding for glyceraldehyde 3--phosphate dehydrogenase) was chosen as the reference gene [42,43]. The relative RNA expression was analyzed using the 2−ΔΔCT method [44] based on the mean ± standard deviation of three biological replicates. The standard errors of deviation were calculated using the STDEVA function in Excel.

3. Results

3.1. Statistic of the Transcriptome under Fire Stimulation

In this study, nine transcriptome libraries of M. macclurei were constructed and more than 20 million clean reads (>6 Gb clean bases) of each library were obtained (Table S2). The error rate was controlled at 0.03. The Q20 and Q30 (the percentage of base with Phred value ≥ 20 and 30) were more than 96% and 91% (Table S2). A total of 226,627 transcripts and 104,052 unigenes was assembled using the clean reads. The number of transcripts (71,670, 31.62% of total) and unigenes (41,511, 39.89% of total) with the length of 301–500 was the highest. As the sequence length increased, the number of transcripts and unigenes decreased (Figure S1a). The BUSCO results of unigene and cluster showed the same number of BUSCOs and percentage of each type. A total of 999 (69.4%) groups were completely aligned, including 972 (67.5%) single copy and 27 (1.9%) duplicated BUSCOs (Figure S1b).

3.2. Gene Functional Annotation and Transcription Factors (TFs) Identification

Gene function annotation in seven major databases showed that all unigenes could be annotated, some of them could be annotated in several databases, and 3602 (3.46% of the total) have the annotation information in all databases; 48.46% of the total was annotated in at least one database (Table 1). The majority of the unigenes were annotated in the NR (39,566, 38.02%), PFAM (29,517, 28.36%) and GO (29,510, 28.36%) databases. There were 22,961, 26,654, 12,577 and 6252 unigenes annotated in NT, SwissProt, KO and KOG, respectively (Table 1). In GO annotation, the annotated genes were classified into 43 terms according to the GO three categories (BP: biological process, CC: cellular component, MF: molecular function). The terms of cellular process, binding, and cellular anatomical entity term contained the most unigenes in BP, MF and CC categories, which were 16,627, 15,472, and 12,385, respectively (Figure 2a).
In the KOG database, 6252 unigenes were annotated and classified into 25 groups. Most unigenes were annotated to groups of ‘posttranslational modification, protein turnover, chaperones’, ‘general function prediction only’, and ‘translation, ribosomal structure and biogenesis’, respectively (Figure 2b). According to the KEGG classification of annotated unigenes, five KO pathways were on level 1, which were further divided into 33 pathways on level 2. The KO pathway metabolism category (level 1) corresponds to 12 pathways (level 2) containing the most unigenes (5865), followed by the organismal systems (2607 genes, 10 pathways), genetic information processing (2537 genes, 4 pathways), cellular processes (1474 genes, 5 pathways), and environmental information processing (1311 genes, 2 pathways). In all pathways, the signal transduction and carbohydrate metabolism were the pathways with the largest number of annotated unigenes, with 1208 and 1118, respectively; by contrast, only a small number of genes were classified on the development (40) and sensory system (16) pathways (belong to the organismal systems) (Figure 2c). A total of 1447 transcription factors across 71 subfamilies have been identified, with 9 including multiple families (MYB, AP2/ERF, C2C2, HB, GARP, B3, NF-Y, SWI/SNF, MADS) (Figure S2a). In each subfamily, the gene number was various, ranging from 1 to 130, and there were 54 families that had more than 10 genes (Figure S2). The MYB subfamily consists of MYB-related (87 genes) and MYB families (43 genes), which has the largest genes number, followed by the subfamilies of AP2/ERF (mainly consisting of AP2/ERF-ERF family, 75 genes) and bHLH subfamily (64 genes) (Figure S2).

3.3. Identification, GO and KEGG Enrichment of Differentially Expressed Genes (DEGs)

In this study, a total of 104,052 unigenes were assembled and counted (Table S3), of which 6574 were identified that differentially expressed in different samples (Table S4). In all samples, 33,151 unigenes were identified and expressed, and the specifically expressed genes in three groups were 9345, 9105, and 9626, respectively (Figure 3a). The histogram revealed a significant number of DEGs, with 4458 and 4810 in the two treatment groups compared to the control group, and 612 in between the two treatment groups (Figure 3b). The DEGs counts in T1 vs. T2 only occupied about 13% of the other two groups. Compared with CK, more genes were down-regulated after heat stress (T1 and T2 samples). In group T1 vs. T2, there were more down-regulated genes in the T2 sample (severe fire treatment) (Figure 3b).
In order to explore the biological functions and pathways significantly related to the DEGs between two samples, GO and KEGG enrichment analyses were performed. The GO enrichment results showed that, in the MF category, ‘oxidoreductase activity’ was the most enriched term among the three comparisons and it was the only term that was enriched between T1 and T2 samples (Figure 4). In the BP category, there were 242 and 152 DEGs enriched in the ‘cellular protein modification process’ and ‘carbohydrate metabolic process’ between CK and T1 samples, respectively. Nevertheless, the ‘cellular protein modification process’ was not enriched in between CK and T2 samples and the majority of genes were mainly enriched in the ‘carbohydrate metabolic process’ and ‘lipid metabolic process’ (Figure 4).
By KEGG enrichment analysis, four common pathways were identified in three comparisons, which were ‘biosynthesis of secondary metabolites’, ‘metabolic pathways’, ‘phenylpropanoid biosynthesis’, and ‘tyrosine metabolism’ (Figure 5). Compared with CK, there were nine common enrichment pathways in the two treated samples, such as ‘plant hormone signal transduction’, ‘indole alkaloid biosynthesis’, and ‘cutin, suberine and wax biosynthesis’. Eight unique pathways were enriched in the comparison of T1 and T2, including ‘monoterpenoid biosynthesis’, ‘MAPK signaling pathway’, and ‘nitrogen metabolism’, etc. (Figure 5). The details of the significantly enriched gene information are listed in Table S5. In the plant hormone signal transduction pathway, the DEGs were mainly enriched in the zeatin biosynthesis and phenylalanine metabolism pathway, which related to the functions of cell division, shoot initiation and disease resistance. The genes of two-component response regulators and signal transduction response regulators were up-regulated after the high heat stress by fire in the zeatin biosynthesis pathway (Figure 6a). In the phenylalanine metabolism pathway, the up-regulated transcription factors TGA2 and TGAL6 increased the expression of pathogenesis-related proteins in treated samples (Figure 6b).

3.4. DEGs Closely Related to Heat Stress Response

In all DEGs, several types of genes showed the specific expression pattern after high-temperature stimulation, including those coding for heat shock factor proteins (HSFs), disease resistance proteins, stress-related proteins, phytohormones, signal transduction, energy metabolism, organelles, and cell growth, TFs, and other vital proteins and kinases (Figure 7). HSFs are the direct response factors under heat stress, the 15.4 kDa class V heat shock protein and HSFB-4b-like protein accumulated under heat stress, but the HSFA-7a-like, HSF30, and HSP83 were down-regulated, and the magnitude of down-regulation was positively correlated with stimulus intensity. In addition, the genes coding for stress-enhanced proteins were up-regulated, such as dehydration-responsive element-binding protein and late embryogenesis abundant protein (Figure 7a). After the heat stress, the gene expression levels of coding for disease resistance proteins that were relevant decreased (Figure 7a). For phytohormones, salicylic acid-binding protein, abscisic acid, and gibberellin-regulated protein were down-regulated in treatment samples. Auxin-related transporter protein, binding protein, induced protein, and responsive protein were down-regulated, but the repressed proteins were up-regulated under heat stress (Figure 7a). In Figure 7b, the calmodulin-binding proteins, electron transporters, ATP synthesis-related genes, chlorophyll binding proteins, and genes involved in cell expansion and growth were decreased after heat stress, but the signal transduction response regulators and general substrate transporters increased. Several main stress-related TFs were also identified, including positively responding NAC 21/22/29/83, WRKY 70/75, AP2/ERF bZIP, and TGAL6, negatively responding WRKY49, MYB44, bHLH118/146, GATA transcription factor 2/8, and ethylene-responsive transcription factors (Figure 7c). In addition, a lot of ABC transporters and cytochrome P450 showed high and different expression levels (Figure 7c). The heat response is a complex process that involves various proteins and pathways; here, we also summarized many high expressions that may regulate the response process. This contained the up-regulated E3 ubiquitin-protein, zinc finger family protein, leucine-rich repeat receptor-like protein kinase, LRR receptor-like serine/threonine-protein kinase, and catalase, down-regulated plant peroxidase, pectinesterase, proline-rich protein, histone, and DNA damage related proteins (Figure 7d).

3.5. qRT-PCR of DEGs for RNA-Seq, Organs and Other Treatments Samples

To validate the reliability of RNA-Seq results, 16 candidate DEGs associated with heat stress response (in Figure 7) were randomly selected for qRT-PCR. They contained heat shock protein, disease resistance protein, stress enhanced protein, genes related to hormones, signal transduction, ATPase, and TFs (Figure 8). Except for the genes of signal transduction response regulator (Cluster-21881.26260), photosystem I reaction center subunit II (Cluster-21881.41436), and bZIP transcription factor 11-like (Cluster-21881.42929), the others showed a good agreement with transcriptome sequencing. These results validated the high consistency expression patterns between RNA-Seq and qRT-PCR, suggesting the accuracy of the transcriptome sequencing in this study.
In addition, qRT-PCR also provided important information on five key genes that be involved in heat stress by fire treatment and conventional heat stress, and the tissue specificity. The results revealed that the coding genes of heat shock protein HSF30, stress enhanced protein, and photosystem I reaction center subunit II exhibited particularities in leaf tissue (Figure 9a). Genes coding for heat shock proteins displayed a distinct expression pattern between fire treatment and conventional heat stress. For example, the expression of the gene that codes heat shock protein (Cluster-21881.35845) increased with the fire treatment time, but did not change significantly in conventional heat treatment experiments (1 h after 40 °C). In contrast, the gene coding for heat shock factor protein HSF30 (Cluster-21881.45799) had an opposite pattern of expression between fire treatment and conventional heat stress, and it was rarely detected in bark and root (Figure 9a–c). Moreover, the genes coding stress enhanced protein (Cluster-21881.39835) and a signal transduction response regulator (Cluster-21881.26260) were up-regulated after fire treatment, wounding, and high-temperature (40 °C) treatment, and the gene related to signal transduction was higher than leaf and root (Figure 9). Finally, the gene involved in the photosystem I reaction (Cluster-21881.41436) was down-regulated after both heat stress and wounding, and had an extremely low expression in the root (Figure 9).

4. Discussion

In nature, plants suffer various environmental conditions and produce a response to these biotic and abiotic stresses, of which the extremely high temperature induced by a forest fire is a huge challenge for the fire-resistance of tree species. Because of the strong fire resistance and excellent landscaping, M. macclurei has been widely planted as the landscape tree species and in fire prevention and belt construction in the southern cities of China, especially in Guangdong province [3,45]. In recent years, the stress on physiological and biochemical events in tree species of the Magnoliaceae family or Michelia genus have been improving [46], such as the components and flammability of the tree, moisture ignition point and ash content [3]. However, the response mechanism of high temperature induced by fire damage has not increased. Based on the tremendous progress in the complex mechanisms underlying heat stress response in other plants, it is possible to investigate more uncharacterized species. In this study, we performed a fire experiment with two intensities and implemented a de novo RNA-seq to obtain the large-scale gene expression information that responds to heat stress induced by fire stimulation in M. macclurei. It is a feasible research method, and has been successfully applied to the study of heat resistance and the response of many species, such as Korean fir [32], dove tree [33], and maize [47]. In this study, 104,052 unigenes were assembled, which was close to that of the same genus species, such as M. maudiae (109,729 unigenes) [48]. Abundant DEGs were identified that may respond to the heat stress, which provides the first insights into the transcriptomic data of M. macclurei under heat stress by fire simulation.
Based on GO enrichment analyses of DEGs, ‘oxidoreductase activity’ was the only enriched term between low and high-intensity treated samples, suggesting that this type of gene was crucial for different stimulus intensities (Figure 4). This finding has been confirmed by studies on other species, such as switchgrass [31], wheat [49], and tomato [50]. In M. macclurei, we summarized 151 core DEGs related to heat stress in Figure 7, of which some were annotated as ‘oxidoreductase activity’ in the MF description (GO annotation). They contained abscisic acid 8′-hydroxylase 4 (Cluster-21881.44942), gibberellin 3-beta-dioxygenase 1-like (Cluster-21881.40336), auxin-binding protein ABP19a (Cluster-21881.43024), 1-aminocyclopropane-1-carboxylate oxidase (Cluster-21881.42816), polyphenol oxidase (Cluster-21881.41351), and alkane hydroxylase MAH1-like protein (Cluster-21881.43720 and Cluster-21881.51339), which were totally down-regulated under heat stress. Five cytochrome P450 genes were also annotated with heme binding/oxidoreductase activity, and showed different expression responses to heat stress (Figure 7c).
In response to heat stress, all major hormones including auxin, gibberellins, ABA, SA, cytokinins, jasmonic acid, ethylene, and brassinosteroids were found to play critical roles [22,51]. DREB2A was positively regulated by abscisic acid and jasmonic acid, which contributes to heat tolerance in plants [52]. In M. macclurei, the dominant hormones responding to heat stress were auxin, gibberellin, abscisic acid, salicylic acid, and cytokinine (Figure 6 and Figure 7a). Heat stress caused a decrease in binding proteins, regulated proteins and an increase in receptors, and repressed proteins, resulting in less synthesis of salicylic acid, gibberellin and auxin (Figure 7a). Auxin co-receptors have been demonstrated to be involved in root and lateral root formation of Arabidopsis in response to HS [53], while a reduced accumulation of GA1 may inhibit panicle expansion in heat-stressed rice [54]. The stress response of M. macclurei to heat shock works mainly through the down-regulation of genes involved in auxin and gibberellin pathways, as well as abscisic acid biosynthesis and catabolism. Abscisic acid 8′-hydroxylase 4, a cytochrome P450 enzyme, is the key factor attributed to higher heat tolerance, likely due to its regulation of abscisic acid synthesis [55,56,57]. Abscisic acid content has been observed to change with time in Grapevine after heat treatment [58]. The inhibition of ABA 8′-hydroxylase has demonstrated dehydration tolerance in grape cuttings [59]. In M. macclurei, KEGG enrichment analysis revealed that the DEGs were mainly enriched in the cytokinine and salicylic acid signal transduction pathways (Figure 6). The down-regulation of the gene related to abscisic acid 8′-hydroxylase may be associated with an increase in ABA accumulation, which could positively regulate the expression of dehydration-responsive elements (DREBs) (Cluster-21881.35071), and thus enhance heat tolerance. Additionally, salicylic acid was shown to interact with ethylene formation and proline metabolism to respond to heat stress in wheat [60]. Meanwhile, reductions in cytokinine levels lead to fewer seed cells and a slower seed growth rate [61]. Here, the down-regulated genes associated with cell wall protein, expansion, actin-related protein and tubulin alpha (Figure 7b), all of which are important for cytoskeleton and cell elongation, have been observed. When exposed to heat stress, the up-regulation of genes related to pathogen-related proteins was accompanied by weakened disease resistance in the salicylic acid pathway. These results suggest that hormone signaling transduction plays an important role in responding to gene expression levels related to cell division, expansion, and disease resistance in M. macclurei under fire heat stress.
In the heat stress response, HSFs are considered central regulators of transcriptional regulation, and are the transcriptional activators of HSPs [62,63]. HSPs control cellular signaling, protein translocation, and degradation; in heat stress they increased the production by preventing protein misfolding and aggregation, and protecting cellular membranes [15]. In M. macclurei, the genes coding of the 15.4 kDa class V heat shock protein (Cluster-21881.35845) and HSFB-4b-like protein accumulated with the increase in the fire treatment time (Figure 7 and Figure 9c), while HSFA-7a-like, HSF30 and HSP83 were down-regulated (Figure 7). However, the gene coding HSF30 (Cluster-21881.45799) had a positive response to conventional heat stress (Figure 9b). Although we did not identify more HSPs in this study, gene expression dynamics and different heat treatments of several core genes revealed the distinct function of HSPs in response to fire treatment and conventional heat stress and the different mechanisms between them. Fire treatments are characterized by high temperatures for a brief period of time, often lasting only a few minutes. In contrast, conventional heat-stress involves sustained high temperatures for extended periods such as 24–72 h [4,9,10]. Therefore, we inferred that fire-related treatments may involve a wider range of responses, such as signal transduction [64] and hormone regulation beyond heat shock response. In addition to HSPs, other non-heat-shocked genes [65] related to ATP synthesis, oxidation reduction, and chlorophyll binding protein were also observed in M. macclurei to be down-regulated (Figure 7b). The activation of HSFs is thought to induce the expression of several functional genes encoding antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), thus amplifying the peroxide signals and mitigating oxidative damages in response to stresses [66,67].
In addition to HSFs, various other TFs are also known to play key roles in plant responses to heat stress by regulating the expression of HS-inducible genes [68]. For example, AP2/ERFs can control the ethylene signaling pathway in stress responses [69,70]. bHLH transcriptional regulator PHYTOCHROME INTERACTING FACTOR 4 (PIF4) can interact with phytochromes and DELLA proteins in Arabidopsis and regulate HS-inducible gene expression in potato [71,72]. Moreover, OsMYB55 was found to be effective in improving rice tolerance to high temperatures when overexpressed [73], and the rice stress-responsive NAC, SNAC3 could enhance tolerance to high temperatures by modulating reactive oxygen species (ROS) homeostasis [74]. Additionally, ZmWRKY106 was reported to improve heat tolerance by regulating stress-related genes and changing the content of ROS, as well as the activities of superoxide dismutase (SOD), peroxide dismutase (POD), and catalase in maize [75]. In M. macclurei, several TFs differentially expressed after heat stress (Figure 7b). These TFs included bZIP 11-like, NAC21/22, NAC29, NAC83-like, WRKY70, WRKY75, and TGAL6, which showed increased expression levels, while bHLH67, bHLH118, bHLH146, and MYB showed decreased expression (Figure 7c). Furthermore, several response genes such as signal transduction response regulator, general substrate transporter, glucose-6-phosphate 1-dehydrogenase, glycoside hydrolase, E3 ubiquitin-protein, zinc finger protein, leucine-rich repeat receptor-like protein kinase, and catalase, electron transporter, calcium uptake protein, calmodulin-binding protein, oxidation resistance protein, plant peroxidase, peptidase, pyruvate kinase, and histone were detected to be up- or down-regulated (Figure 7b,d). Such findings demonstrate that TFs play an important role in helping plants respond to thermally stressed conditions, and may provide more efficient management strategies to cope with heat stress in forest trees.

5. Conclusions

The extreme heat caused by fire is one of the main stresses for fire-resistant tree species. The M. macclurei is widely cultivated for fire-resistant tree species, but the mechanism of responding to a high temperature caused by fire is unclear. This study investigated the gene regulatory network of M. macclurei leaves under heat stress by fire treatment, and provided abundant transcriptomic data to further explore the molecular mechanism of heat resistance in fire-resistant tree species. Through RNA-Seq and qRT-PCR, we identified abundant differentially expressed genes and detected crucial genes with tissue specificity, gene expression dynamics and comparison with other stress treatments. GO enrichment revealed the role of genes involved in oxidoreductase activity in M. macclurei responses to high-temperature stress. KEGG enrichment analysis showed the importance of plant hormone signal transduction mediated in the heat response. Additionally, HSFs, major TFs, and genes related to cellular process, photosystem and disease resistance had different expression patterns, suggesting the complexity of the regulatory network.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14030610/s1, Figure S1: Transcript length distribution and quality assessment. (a) Transcript length distribution. The horizontal axis is the length interval of transcripts, and the vertical axis is the number of transcripts. (b) BUSCO assessment results of transcripts. Different colors represent different types of transcripts, respectively. S: complete single-copy BUSCOs; D: complete duplicated BUSCOs; F: fragmented BUSCOs; M: missing BUSCOs; n: total BUSCO groups searched. Figure S2: Classification and statistics of transcription factors (TFs) in Michelia macclurei. The horizontal axis is TF families, and the vertical axis is the number of TFs. Table S1: Primers used for qRT-PCR analysis. Table S2: Summary of RNA-Seq data in Michelia macclurei. Table S3: Annotation of unigenes in different samples. Table S4: Differentially expressed genes under heat stress in Michelia macclurei. Table S5: KEGG enrichment information of differentially expressed genes.

Author Contributions

S.W. and R.L. conceived and designed the experiment. Y.Z. (Yingxia Zhong) and Y.Z. (Yufei Zhou) performed the experiments and collected the samples. Z.S. and S.L. analyzed the data. S.W. and R.L. wrote the manuscript. Z.S. and S.L. revised the manuscript. Y.Z. (Yingxia Zhong) and Y.Z. (Yufei Zhou) reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Guangdong Province, China (No. 2021A1515010946), and the forestry science and technology innovation of Guangdong Province, China (No. 2020KJCX003).

Data Availability Statement

All data are available on reasonable request to the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, X.; Liao, L.N.; Yang, H.P.; Huang, Y.Z.; He, A.J.; Ye, S.M. Spatial pattern of artificial mixed forest of Cunninghamia lanceolata and Michelia macclurei. J. Southwest For. Univ. 2020, 40, 1–9. [Google Scholar]
  2. Lan, X.; Liang, Y.; Wei, Z.; Yu, G. Biological characteristics of Michelia macclurei Dandy and its utilization prospects. Guangxi Agric. Sci. 2010, 41, 253–255. [Google Scholar]
  3. Bhu, L.; Tian, X.; Kou, X. Studies on selection of fire resistance tree species for sub-tropical area of China. Fire Saf. Sci. 2000, 4, 181–190. [Google Scholar]
  4. Michaletz, S.T.; Johnson, E.A. How forest fires kill trees: A review of the fundamental biophysical processes. Scand. J. For. Res. 2007, 22, 500–515. [Google Scholar] [CrossRef]
  5. Ne′eman, G.; Goubitz, S.; Nathan, R. Reproductive traits of Pinus halepensis in the light of fire—A critical review. Plant Ecol. 2004, 171, 69–79. [Google Scholar] [CrossRef]
  6. Kodandapani, N.; Cochrane, M.A.; Sukumar, R. A comparative analysis of spatial, temporal, and ecological characteristics of forest fires in seasonally dry tropical ecosystems in the Western Ghats, India. For. Ecol. Manag. 2008, 256, 607–617. [Google Scholar] [CrossRef]
  7. Ryan, K.C.; Reinhardt, E.D. Predicting postfire mortality of seven western conifers. Can. J. For. Res. 1988, 18, 1291–1297. [Google Scholar] [CrossRef]
  8. Parker, T.J.; Clancy, K.M.; Mathiasen, R.L. Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada. Agric. For. Entomol. 2006, 8, 167–189. [Google Scholar] [CrossRef]
  9. Bär, A.; Michaletz, S.T.; Mayr, S. Fire effects on tree physiology. New Phytol. 2019, 223, 1728–1741. [Google Scholar] [CrossRef] [Green Version]
  10. Wahid, A.; Gelani, S.; Ashraf, M.; Foolad, M.R. Heat tolerance in plants: An overview. Environ. Exp. Bot. 2007, 61, 199–223. [Google Scholar] [CrossRef]
  11. Schoffl, F.; Prandl, R.; Reindl, A. Regulation of the heat-shock response. Plant Physiol. 1998, 117, 1135–1141. [Google Scholar] [CrossRef] [Green Version]
  12. Howarth, C. Genetic Improvements of Tolerance to High Temperature. In Abiotic Stresses: Plant Resistance Through Breeding And Molecular Approaches; Howarth Press Inc.: New York, NY, USA, 2005; pp. 277–300. [Google Scholar]
  13. Kotak, S.; Larkindale, J.; Lee, U.; von Koskull-Döring, P.; Vierling, E.; Scharf, K.D. Complexity of the heat stress response in plants. Curr Opin. Plant Biol. 2007, 10, 310–316. [Google Scholar] [CrossRef] [PubMed]
  14. Haider, S.; Iqbal, J.; Naseer, S.; Shaukat, M.; Abbasi, B.A.; Yaseen, T.; Zahra, S.A.; Mahmood, T. Unfolding molecular switches in plant heat stress resistance: A comprehensive review. Plant Cell Rep. 2022, 41, 775–798. [Google Scholar] [CrossRef]
  15. Nover, L.; Bharti, K.; Döring, P.; Mishra, S.K.; Ganguli, A.; Scharf, K.D. Arabidopsis and the heat stress transcription factor world: How many heat stress transcription factors do we need? Cell Stress Chaperon. 2001, 6, 177. [Google Scholar] [CrossRef]
  16. Mishra, S.K.; Tripp, J.; Winkelhaus, S.; Tschiersch, B.; Theres, K.; Nover, L.; Scharf, K.D. In the complex family of heat stress transcription factors, HsfA1 has a unique role as master regulator of thermotolerance in tomato. Gene. Dev. 2002, 16, 1555–1567. [Google Scholar] [CrossRef] [Green Version]
  17. Nishizawa, A.; Yabuta, Y.; Yoshida, E.; Maruta, T.; Yoshimura, K.; Shigeoka, S. Arabidopsis heat shock transcription factor A2 as a key regulator in response to several types of environmental stress. Plant J. 2006, 48, 535–547. [Google Scholar] [CrossRef] [PubMed]
  18. Andrási, N.; Pettkó-Szandtner, A.; Szabados, L. Diversity of plant heat shock factors: Regulation, interactions, and functions. J. Exp. Bot. 2021, 72, 1558–1575. [Google Scholar] [CrossRef] [PubMed]
  19. Scarpeci, T.E.; Zanor, M.I.; Valle, E.M. Investigating the role of plant heat shock proteins during oxidative stress. Plant Signal Behav. 2008, 3, 856–857. [Google Scholar] [CrossRef] [Green Version]
  20. Liu, H.T.; Sun, D.Y.; Zhou, R.G. Ca2+ and AtCaM3 are involved in the expression of heat shock protein gene in Arabidopsis. Plant Cell Environ. 2005, 28, 1276–1284. [Google Scholar] [CrossRef]
  21. Saidi, Y.; Finka, A.; Muriset, M.; Bromberg, Z.; Weiss, Y.G.; Maathuis, F.J.; Goloubinoff, P. The heat shock response in moss plants is regulated by specific calcium-permeable channels in the plasma membrane. Plant Cell 2009, 21, 2829–2843. [Google Scholar] [CrossRef] [Green Version]
  22. Larkindale, J.; Knight, M.R. Protection against heat stress-induced oxidative damage in Arabidopsis involves calcium, abscisic acid, ethylene, and salicylic acid. Plant Physiol. 2002, 128, 682–695. [Google Scholar] [CrossRef] [PubMed]
  23. Volkov, R.A.; Panchuk, I.I.; Mullineaux, P.M.; Schöffl, F. Heat stress-induced H2O2 is required for effective expression of heat shock genes in Arabidopsis. Plant Mol. Biol. 2006, 61, 733–746. [Google Scholar] [CrossRef]
  24. Jacob, P.; Hirt, H.; Bendahmane, A. The heat-shock protein/chaperone network and multiple stress resistance. Plant Biotechnol. J. 2017, 15, 405–414. [Google Scholar] [CrossRef] [Green Version]
  25. Liu, H.T.; Gao, F.; Li, G.L.; Han, J.L.; Liu, D.L.; Sun, D.Y.; Zhou, R.G. The calmodulin-binding protein kinase 3 is part of heat-shock signal transduction in Arabidopsis thaliana. Plant J. 2008, 55, 760–773. [Google Scholar] [CrossRef] [PubMed]
  26. Rai, K.K.; Pandey, N.; Rai, S.P. Salicylic acid and nitric oxide signaling in plant heat stress. Physiol. Plant. 2020, 168, 241–255. [Google Scholar] [CrossRef]
  27. Jha, U.C.; Nayyar, H.; Siddique, K.H. Role of phytohormones in regulating heat stress acclimation in agricultural crops. J. Plant Growth Regul. 2022, 41, 1041–1064. [Google Scholar] [CrossRef]
  28. Poór, P.; Nawaz, K.; Gupta, R.; Ashfaque, F.; Khan, M.I.R. Ethylene involvement in the regulation of heat stress tolerance in plants. Plant Cell Rep. 2022, 41, 675–698. [Google Scholar] [CrossRef]
  29. Wang, L.; Ma, K.B.; Lu, Z.G.; Ren, S.X.; Jiang, H.R.; Cui, J.W.; Chen, G.; Teng, N.J.; Lam, H.M.; Jin, B. Differential physiological, transcriptomic and metabolomic responses of Arabidopsis leaves under prolonged warming and heat shock. BMC Plant Biol. 2020, 20, 86. [Google Scholar] [CrossRef] [PubMed]
  30. Korotaeva, N.E.; Gette, I.G.; Pacharkova, N.V.; Kosov, I.V.; Borovskii, G.B. Short-and long-term effects of surface fires on heat stress protein content in Scots pine needles. Int. J. Wildland Fire 2021, 30, 978–989. [Google Scholar] [CrossRef]
  31. Li, Y.F.; Wang, Y.; Tang, Y.; Kakani, V.G.; Mahalingam, R. Transcriptome analysis of heat stress response in switchgrass (Panicum virgatum L.). BMC Plant Biol. 2013, 13, 153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Hwang, J.E.; Kim, Y.J.; Shin, M.H.; Hyun, H.J.; Bohnert, H.J.; Park, H.C. A comprehensive analysis of the Korean fir (Abies koreana) genes expressed under heat stress using transcriptome analysis. Sci. Rep. 2018, 8, 10233. [Google Scholar] [CrossRef] [Green Version]
  33. Liu, Q.; Vetukuri, R.R.; Xu, W.; Xu, X. Transcriptomic responses of dove tree (Davidia involucrata Baill.) to heat stress at the seedling stage. Forests 2019, 10, 656. [Google Scholar] [CrossRef] [Green Version]
  34. Kuang, Y.; Xu, Y.; Zhang, L.; Hou, E.; Shen, W. Dominant trees in a subtropical forest respond to drought mainly via adjusting tissue soluble sugar and proline content. Front. Plant Sci. 2017, 8, 802. [Google Scholar]
  35. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Davidson, N.M.; Oshlack, A. Corset: Enabling differential gene expression analysis for de novo assembled transcriptomes. Genome Biol. 2014, 15, 410. [Google Scholar] [PubMed] [Green Version]
  37. Manni, M.; Berkeley, M.R.; Seppey, M.; Zdobnov, E.M. BUSCO: Assessing genomic data quality and beyond. Curr. Protoc. 2021, 1, e323. [Google Scholar] [CrossRef]
  38. Pérez-Rodríguez, P.; Riano-Pachon, D.M.; Corrêa, L.G.G.; Rensing, S.A.; Kersten, B.; Mueller-Roeber, B. PlnTFDB: Updated content and new features of the plant transcription factor database. Nucleic Acids Res. 2010, 38, D822–D827. [Google Scholar] [CrossRef] [Green Version]
  39. Jin, J.; Zhang, H.; Kong, L.; Gao, G.; Luo, J. PlantTFDB 3.0: A portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 2014, 42, D1182–D1187. [Google Scholar] [CrossRef] [Green Version]
  40. Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [Green Version]
  41. Rozen, S.; Skaletsky, H. Primer3 on the WWW for General Users and for Biologist Programmers. In Bioinformatics Methods and Protocols; Springer: Berlin/Heidelberg, Germany, 2000; pp. 365–386. [Google Scholar]
  42. Liu, C.; Yu, Q.; Li, Z.; Jin, X.; Xing, W. Metabolic and transcriptomic analysis related to flavonoid biosynthesis during the color formation of Michelia crassipes tepal. Plant Physiol. Bioch. 2020, 155, 938–951. [Google Scholar] [CrossRef]
  43. Zhou, W.; Duan, Y.; Jiang, X.; Tan, X.; Li, Q.; Wang, H.; Zhang, Y.; Zhang, M. Transcriptome and metabolome analyses reveal novel insights into the seed germination of Michelia chapensis, an endangered species in China. Plant Sci. 2022, 328, 111568. [Google Scholar] [CrossRef] [PubMed]
  44. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  45. Jiangwei, L.; Xueying, L.; Zhibiao, X.; Honglan, H. A Study on introduction and adaptation of Magnoliaceae plants and its landscape evaluation. For. Resour. Wanagement 2020, 161–168. [Google Scholar] [CrossRef]
  46. Kaiyue, H.; Xiaochu, L.; Libin, H.; Yongbing, Z.; Xiaojian, H. Effects of drought stress on physiological and biochemical indices in five tree species of Magnoliaceae. J. Plant Resour. Environ. 2004, 13, 20–23. [Google Scholar]
  47. Li, Z.G.; Ye, X.Y. Transcriptome response of maize (Zea mays L.) seedlings to heat stress. Protoplasma 2022, 259, 357–369. [Google Scholar] [CrossRef] [PubMed]
  48. Lang, X.; Li, N.; Li, L.; Zhang, S. Integrated metabolome and transcriptome analysis uncovers the role of anthocyanin metabolism in Michelia maudiae. Int. J. Genom. 2019, 2019, 4393905. [Google Scholar] [CrossRef] [Green Version]
  49. Hlaváčová, M.; Klem, K.; Rapantová, B.; Novotná, K.; Urban, O.; Hlavinka, P.; Smutná, P.; Horáková, V.; Škarpa, P.; Pohanková, E. Interactive effects of high temperature and drought stress during stem elongation, anthesis and early grain filling on the yield formation and photosynthesis of winter wheat. Field Crop. Res. 2018, 221, 182–195. [Google Scholar] [CrossRef]
  50. Su, H.Z.; Ma, S.Y.; Ma, X.H.; Song, Y.; Wang, X.M.; Cheng, G.X. Transcriptome analyses show changes in heat-stress related gene expression in tomato cultivar ‘Moneymaker’under high temperature. J. Plant Biochem. Biot. 2022, 1–10. [Google Scholar] [CrossRef]
  51. Ahammed, G.J.; Li, X.; Zhou, J.; Zhou, Y.-H.; Yu, J.Q. Role of Hormones in Plant Adaptation to Heat Stress. In Plant Hormones Under Challenging Environmental Factors; Springer: Berlin/Heidelberg, Germany, 2016; pp. 1–21. [Google Scholar]
  52. Zhang, X.; Wang, X.; Zhuang, L.; Gao, Y.; Huang, B. Abscisic acid mediation of drought priming-enhanced heat tolerance in tall fescue (Festuca arundinacea) and Arabidopsis. Physiol. Plant. 2019, 167, 488–501. [Google Scholar] [CrossRef]
  53. Wang, R.; Zhang, Y.; Kieffer, M.; Yu, H.; Kepinski, S.; Estelle, M. HSP90 regulates temperature-dependent seedling growth in Arabidopsis by stabilizing the auxin co-receptor F-box protein TIR1. Nat. Commun. 2016, 7, 11677. [Google Scholar] [CrossRef]
  54. Wu, C.; Cui, K.; Wang, W.; Li, Q.; Fahad, S.; Hu, Q.; Huang, J.; Nie, L.; Peng, S. Heat-induced phytohormone changes are associated with disrupted early reproductive development and reduced yield in rice. Sci. Rep. 2016, 6, 34978. [Google Scholar] [CrossRef] [Green Version]
  55. Saito, S.; Hirai, N.; Matsumoto, C.; Ohigashi, H.; Ohta, D.; Sakata, K.; Mizutani, M. Arabidopsis CYP707As encode (+)-abscisic acid 8′-hydroxylase, a key enzyme in the oxidative catabolism of abscisic acid. Plant Physiol. 2004, 134, 1439–1449. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Nambara, E.; Marion-Poll, A. Abscisic acid biosynthesis and catabolism. Annu. Rev. Plant Biol. 2005, 56, 165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Sharma, E.; Borah, P.; Kaur, A.; Bhatnagar, A.; Mohapatra, T.; Kapoor, S.; Khurana, J.P. A comprehensive transcriptome analysis of contrasting rice cultivars highlights the role of auxin and ABA responsive genes in heat stress response. Genomics 2021, 113, 1247–1261. [Google Scholar] [CrossRef]
  58. Guo, R.; Lin, L.; Huang, G.; Shi, X.; Wei, R.; Han, J.; Zhou, S.; Zhang, Y.; Xie, T.; Bai, X. Transcriptome Analysis of ‘Kyoho’ grapevine leaves identifies heat response genes involved in the transcriptional regulation of photosynthesis and abscisic acid. Agronomy 2022, 12, 2591. [Google Scholar] [CrossRef]
  59. Tomiyama, H.; Sato, M.; Opio, P.; Saito, T.; Ohkawa, K.; Ohara, H.; Todoroki, Y.; Kondo, S. Inhibition of abscisic acid 8′-hydroxylase affects dehydration tolerance and root formation in cuttings of grapes (Vitis labrusca L.× Vitis vinifera L. cv. Kyoho) under drought stress conditions. J. Plant Growth Regul. 2020, 39, 1577–1586. [Google Scholar] [CrossRef]
  60. Khan, M.I.R.; Iqbal, N.; Masood, A.; Per, T.S.; Khan, N.A. Salicylic acid alleviates adverse effects of heat stress on photosynthesis through changes in proline production and ethylene formation. Plant Signal Behav. 2013, 8, e26374. [Google Scholar] [CrossRef] [Green Version]
  61. Jameson, P.E.; Song, J. Cytokinin: A key driver of seed yield. J. Exp. Bot. 2016, 67, 593–606. [Google Scholar] [CrossRef] [Green Version]
  62. Banti, V.; Mafessoni, F.; Loreti, E.; Alpi, A.; Perata, P. The heat-inducible transcription factor HsfA2 enhances anoxia tolerance in Arabidopsis. Plant Physiol. 2010, 152, 1471–1483. [Google Scholar] [CrossRef] [Green Version]
  63. Ohama, N.; Sato, H.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Transcriptional regulatory network of plant heat stress response. Trends Plant Sci. 2017, 22, 53–65. [Google Scholar] [CrossRef]
  64. Guihur, A.; Rebeaud, M.E.; Goloubinoff, P. How do plants feel the heat and survive? Trends Biochem. Sci. 2022, 47, 824–838. [Google Scholar] [CrossRef]
  65. Fritah, S.; Col, E.; Boyault, C.; Govin, J.; Sadoul, K.; Chiocca, S.; Christians, E.; Khochbin, S.; Jolly, C.; Vourc′h, C. Heat-shock factor 1 controls genome-wide acetylation in heat-shocked cells. Mol. Biol. Cell 2009, 20, 4976–4984. [Google Scholar] [CrossRef] [Green Version]
  66. Locato, V.; Gadaleta, C.; De Gara, L.; De Pinto, M.C. Production of reactive species and modulation of antioxidant network in response to heat shock: A critical balance for cell fate. Plant Cell Environ. 2008, 31, 1606–1619. [Google Scholar] [CrossRef]
  67. Chen, S.; Yu, M.; Li, H.; Wang, Y.; Lu, Z.; Zhang, Y.; Liu, M.; Qiao, G.; Wu, L.; Han, X. SaHsfA4c from Sedum alfredii Hance enhances cadmium tolerance by regulating ROS-scavenger activities and heat shock proteins expression. Front. Plant Sci. 2020, 11, 142. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Zhao, Y.; Tian, X.; Wang, F.; Zhang, L.; Xin, M.; Hu, Z.; Yao, Y.; Ni, Z.; Sun, Q.; Peng, H. Characterization of wheat MYB genes responsive to high temperatures. BMC Plant Biol. 2017, 17, 208. [Google Scholar] [CrossRef] [PubMed]
  69. Müller, M.; Munné-Bosch, S. Ethylene response factors: A key regulatory hub in hormone and stress signaling. Plant Physiol. 2015, 169, 32–41. [Google Scholar] [CrossRef] [Green Version]
  70. Xie, Z.; Nolan, T.M.; Jiang, H.; Yin, Y. AP2/ERF transcription factor regulatory networks in hormone and abiotic stress responses in Arabidopsis. Front. Plant Sci. 2019, 10, 228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Koini, M.A.; Alvey, L.; Allen, T.; Tilley, C.A.; Harberd, N.P.; Whitelam, G.C.; Franklin, K.A. High temperature-mediated adaptations in plant architecture require the bHLH transcription factor PIF4. Curr. Biol. 2009, 19, 408–413. [Google Scholar] [CrossRef] [Green Version]
  72. Wang, R.; Zhao, P.; Kong, N.; Lu, R.; Pei, Y.; Huang, C.; Ma, H.; Chen, Q. Genome-wide identification and characterization of the potato bHLH transcription factor family. Genes 2018, 9, 54. [Google Scholar] [CrossRef] [Green Version]
  73. El-Kereamy, A.; Bi, Y.M.; Ranathunge, K.; Beatty, P.H.; Good, A.G.; Rothstein, S.J. The rice R2R3-MYB transcription factor OsMYB55 is involved in the tolerance to high temperature and modulates amino acid metabolism. PLoS ONE 2012, 7, e52030. [Google Scholar] [CrossRef] [Green Version]
  74. Fang, Y.; Liao, K.; Du, H.; Xu, Y.; Song, H.; Li, X.; Xiong, L. A stress-responsive NAC transcription factor SNAC3 confers heat and drought tolerance through modulation of reactive oxygen species in rice. J. Exp. Bot. 2015, 66, 6803–6817. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Wang, C.T.; Ru, J.N.; Liu, Y.W.; Li, M.; Zhao, D.; Yang, J.F.; Fu, J.D.; Xu, Z.S. Maize WRKY transcription factor ZmWRKY106 confers drought and heat tolerance in transgenic plants. Int. J. Mol. Sci. 2018, 19, 3046. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. The Michelia macclurei seedling and sampling area after the fire experiment. Left: the characteristic of the seedling. Right: three leaves showed the samples of CK, T1 (slight fire), and T2 (strong fire). The red frames point to the sampling area.
Figure 1. The Michelia macclurei seedling and sampling area after the fire experiment. Left: the characteristic of the seedling. Right: three leaves showed the samples of CK, T1 (slight fire), and T2 (strong fire). The red frames point to the sampling area.
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Figure 2. Gene Ontology (GO), KOG (euKaryotic Ortholog Groups) and KEGG (Kyoto Encyclopedia of Genes and Genomes) classification of Michelia macclurei unigenes.
Figure 2. Gene Ontology (GO), KOG (euKaryotic Ortholog Groups) and KEGG (Kyoto Encyclopedia of Genes and Genomes) classification of Michelia macclurei unigenes.
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Figure 3. Number of DEGs under the heat stress conditions in Michelia macclurei. (a) Venn diagram analysis of DEGs of three samples. (b) The column diagram in three comparisons between two samples. The x-axis is each comparison, and the y-axis represents the up- and down-regulated gene numbers.
Figure 3. Number of DEGs under the heat stress conditions in Michelia macclurei. (a) Venn diagram analysis of DEGs of three samples. (b) The column diagram in three comparisons between two samples. The x-axis is each comparison, and the y-axis represents the up- and down-regulated gene numbers.
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Figure 4. The GO enrichment analysis of differential expressed genes in Michelia macclurei. (a–c) represents the three comparisons of CK vs. T1, CK vs. T2, and T1 vs. T2.
Figure 4. The GO enrichment analysis of differential expressed genes in Michelia macclurei. (a–c) represents the three comparisons of CK vs. T1, CK vs. T2, and T1 vs. T2.
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Figure 5. The scattering map of KEGG enrichment of differentially expressed genes in Michelia macclurei. The vertical axis indicates the pathway name, and the horizontal axis represents the gene ratio of the corresponding pathway. The Q-value was shown by the color; the smaller the Q-value, the closer the color is to red. The point size represents the number of DEGs. (a–c) represents the three comparisons of CK vs. T1, CK vs. T2, and T1 vs. T2.
Figure 5. The scattering map of KEGG enrichment of differentially expressed genes in Michelia macclurei. The vertical axis indicates the pathway name, and the horizontal axis represents the gene ratio of the corresponding pathway. The Q-value was shown by the color; the smaller the Q-value, the closer the color is to red. The point size represents the number of DEGs. (a–c) represents the three comparisons of CK vs. T1, CK vs. T2, and T1 vs. T2.
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Figure 6. Enriched KEGG pathway terms involved in plant hormone signal transduction (a): cytokinin; (b): salicylic acid) after heat stress in Michelia macclurei. The expression level of the DEGs is shown in the heatmap beside the key nodes in each pathway; the change from blue to red means the expression level is going from low to high.
Figure 6. Enriched KEGG pathway terms involved in plant hormone signal transduction (a): cytokinin; (b): salicylic acid) after heat stress in Michelia macclurei. The expression level of the DEGs is shown in the heatmap beside the key nodes in each pathway; the change from blue to red means the expression level is going from low to high.
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Figure 7. Heatmap of the DEGs associated with heat response in Michelia macclurei. Red means up-regulation, and blue means down-regulation. (a) The genes that code heat shock proteins, disease resistance proteins, stress enhanced proteins, and hormone synthesis. (b) The genes related to signal transduction, energy metabolism, photosynthesis, and cell elongation; (c) Main transcription factors, ABC transporters, and cytochrome; (d) The genes related to oxidation reduction and heat stress resposne.
Figure 7. Heatmap of the DEGs associated with heat response in Michelia macclurei. Red means up-regulation, and blue means down-regulation. (a) The genes that code heat shock proteins, disease resistance proteins, stress enhanced proteins, and hormone synthesis. (b) The genes related to signal transduction, energy metabolism, photosynthesis, and cell elongation; (c) Main transcription factors, ABC transporters, and cytochrome; (d) The genes related to oxidation reduction and heat stress resposne.
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Figure 8. qRT-PCR validation of 16 DEGs involved in heat response in Michelia macclurei. The x-axis is the three samples, and the y-axis shows the FPKM by RNA-Seq (right) and the relative quantitative expression level by qRT-PCR (left) for each unigene.
Figure 8. qRT-PCR validation of 16 DEGs involved in heat response in Michelia macclurei. The x-axis is the three samples, and the y-axis shows the FPKM by RNA-Seq (right) and the relative quantitative expression level by qRT-PCR (left) for each unigene.
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Figure 9. qRT-PCR of five DEGs in organs, wounding stimulation and various heat treatments in Michelia macclurei. The x-axis has the gene names of heat shock protein (Cluster-21881.35845), heat shock factor protein HSF30 (Cluster-21881.45799), stress enhanced protein 2 (Cluster-21881.39835), signal transduction response regulator (Cluster-21881.26260), and photosystem I reaction center subunit II (Cluster-21881.41436). The y-axis shows the relative quantitative expression level by qRT-PCR for each unigene. (a) Tissue specificity of DEGs; (b) gene expression of 1 h after wounding and conventional heat stress; (c) gene expression dynamics of 10 s, 20 s and 30 s, respectively. CK is the normal leaf.
Figure 9. qRT-PCR of five DEGs in organs, wounding stimulation and various heat treatments in Michelia macclurei. The x-axis has the gene names of heat shock protein (Cluster-21881.35845), heat shock factor protein HSF30 (Cluster-21881.45799), stress enhanced protein 2 (Cluster-21881.39835), signal transduction response regulator (Cluster-21881.26260), and photosystem I reaction center subunit II (Cluster-21881.41436). The y-axis shows the relative quantitative expression level by qRT-PCR for each unigene. (a) Tissue specificity of DEGs; (b) gene expression of 1 h after wounding and conventional heat stress; (c) gene expression dynamics of 10 s, 20 s and 30 s, respectively. CK is the normal leaf.
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Table
Table 1. Statistics of unigenes annotation in Michelia macclurei.
Table 1. Statistics of unigenes annotation in Michelia macclurei.
Number of UnigenesPercentage of the Total (%)
Annotated in NR39,56638.02
Annotated in NT22,96122.06
Annotated in KO12,57712.08
Annotated in SwissProt26,65425.61
Annotated in PFAM29,51728.36
Annotated in GO29,51028.36
Annotated in KOG62526
Annotated in all databases36023.46
Annotated in at least one database50,42748.46
Total unigenes104,052100
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MDPI and ACS Style

Wei, S.; Song, Z.; Luo, S.; Zhong, Y.; Zhou, Y.; Lu, R. Transcriptome Analysis Reveals the Heat Stress Response Genes by Fire Stimulation in Michelia macclurei Dandy. Forests 2023, 14, 610. https://doi.org/10.3390/f14030610

AMA Style

Wei S, Song Z, Luo S, Zhong Y, Zhou Y, Lu R. Transcriptome Analysis Reveals the Heat Stress Response Genes by Fire Stimulation in Michelia macclurei Dandy. Forests. 2023; 14(3):610. https://doi.org/10.3390/f14030610

Chicago/Turabian Style

Wei, Shujing, Zhao Song, Sisheng Luo, Yingxia Zhong, Yufei Zhou, and Ruisen Lu. 2023. "Transcriptome Analysis Reveals the Heat Stress Response Genes by Fire Stimulation in Michelia macclurei Dandy" Forests 14, no. 3: 610. https://doi.org/10.3390/f14030610

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